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The Design of Control Strategy for Blended Series-Parallel Power-Split PHEV – a Simulation Study

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The Design of Control Strategy for Blended Series-Parallel Power-Split PHEV – a Simulation Study International Journal of Transportation Systems Ning Ding, K. Prasad, T. T. Lie http://www.iaras.org/iaras/journals/ijts The Design of Control Strategy for Blended Series-Parallel Power-Split PHEV – a Simulation Study Ning Ding, K. Prasad and T.T. Lie Department of Electrical and Electronic Engineering Auckland University of Technology 34 St Paul Street, Auckland New Zealand [email protected] Abstract: - Electric Vehicles (EVs) have been extensively researched to reduce the fuel consumption and tailpipe emission. The series-parallel power-split Plug-in Electric Vehicle (PHEV) has been considered as one of the most suitable candidates. It contains both an internal combustion engine (ICE) and an electrical storage system (ESS) to achieve a better driving performance. The energy management system (EMS) is significant for a PHEV to improve the efficiency of the whole system. Electric vehicle mode (EV), charging depletion (CD) and charging sustaining (CS) modes will be discussed to build a control strategy in this study. This control strategy will be implemented with the state of charge (SoC) to show its impact through a simulation study. KeyWords: - Blended mode; Control strategy; Emission; Fuel economy; PHEV; Simulation; SoC 1 Introduction The PHEV inherits the features of HEV that includes The issues on Global Warming resulted from burning both Internal Combustion Engine (ICE) and Energy of fossil fuel has been discussed for decades. The Storage System (ESS). Fig.1 shows the connecting Electric Vehicle (EVs) as a potential candidate to status for PHEV [7]. The PHEV introduces the grid- reduce the air pollution from traffic section and to able battery technology to extend the energy density relieve the pressure from energy exhaustion have in a euphemistical way [8]. The fundamental been researched extensively [1-3]. Based on the connecting modes between ICE and battery are forms of energy transmission systems, the EVs could similar with conventional HEV, while the grid-able be categorized into three different types: Pure/Battery battery changes the working principle. Electric Vehicle (BEV), Fuel Cell Electric Vehicle Fuel Engine (FCEV) and Hybrid Electric Vehicles (HEV) [4]. tank With the different control strategy and various selections of the components, the performance of Power External EVs will be different, especially in terms of fuel Generator -split consumption and tailpipe emission [5]. The Plug-in power supply Hybrid Electric Vehicle (PHEV) take the advantage Transmission of hybrid energy resources promise to the future transport utilization. The optimal internal energy Battery Invertor Motor management of PHEV has been widely discussed[6]. Fig.1 Series-parallel PHEV In this paper, the series-parallel structures of the PHEVs will be briefly introduced firstly. Taking the 2.1 Blended mode for the series-parallel advantages of blended mode in series-parallel power- power-split PHEV split PHEV, an optimization design of internal energy For the series-parallel power-split PHEV, a complex management will be demonstrated next. The final system cannot be avoided. Since the grid-able battery part will focus on the simulation results and has been introduced, the primary working principle discussion and conclusions from this work. of the PHEV differs from other types of EVs. The working state in PHEV includes three different types: (1) EV mode where the propulsion is solely provided 2 Optimization Design for Series- by battery pack, (2) Charge Depletion (CD) mode where both battery and engine work in a parallel parallel Power-split PHEV structure with the electricity as the main power, and (3) Charge Sustaining (CS) mode where the engine ISSN: 2534-8876 21 Volume 2, 2017 International Journal of Transportation Systems Ning Ding, K. Prasad, T. T. Lie http://www.iaras.org/iaras/journals/ijts SoCi provides a considerable propulsion despite being the SoCi SoCi main provider [7]. In order to improve the efficiency SoCl SoCh and to achieve reduced fuel consumption and emission and to ensure a better vehicle performance Pd/req Pd/req Pd/req at the same time, EMS seems important and EMS Pb,min Pb, max optimization has been extensively researched [9-13]. EV mode 2.2 The EMS optimization models Battery fully provides power EMS relies on several parameters. A critical for the vehicle; ICE is not parameter introduced to describe the instantaneous working. amount of electricity stored in the battery is the SoC. Peng=0 (Pd/req=Psoci) The EV and CD modes take priority in order to CD mode reduce the fuel consumption and emission as much as Battery fully provides power; possible. The CS mode allows the ICE to recharge ICE only provides the left to the battery when the SoC is lower. Otherwise, the achieve the power demand. ICE and battery work collectively when the power Peng= Pd/req -Pb,max demand is high [11, 12]. CSb mode Battery fully provides main 2.2.1 Rule-based SoC control strategy design power for the vehicle; ICE The design concepts for the strategy set the SoC as charging the battery until the priority and then consider the relationship achieves the point of SoCl. between the power requirements and the maximum Peng=( Pd/req-Pb,i)+(Pb,i-PSoCl) power of the battery. Another significant purpose is CSeng2b mode lasting the time of pure electric propulsion and ICE provides main power for charging depletion as long as possible. In other vehicle preferentially, then words, it increases the percentage of EV and CD charging the battery till it is mode in the total logic control. reaching the SoCl. Peng-Pd/req=Peng2b (Peng2c≤ SoCl) Conditions Output mode Fig.2 Design logic of SoC control strategy SoC > SoC EV mode ( battery { ℎ Fig.2 illustrates the design of SoC control strategy P < 푃 working solely ) d/req 푏,푚푎푥 while the parameters used have been listed in Table 1. Based on the SoC strategy, Fig.3 shows the SoC < SoC CS mode { 푙 b functional relationships between CD/CS mode and P < 푃 (optimized output) d/req 푏,푚푎푥 the strategical conditions. SoC < SoC푙 CSeng2b mode { Pd/req > 푃푏,푚푎푥 (battery protection) 3 Simulation Study CD mode (both ICE The initial simulation model is based on the Other and battery ADVISOR (Advanced Vehicle SimulatOR) in working) MATLAB/Simulink operating environment. The Fig.3 Functional relationships for offline CD mode strategy of SoC model will be set in optimization MATLAB/Simulink with Simdriveline models which will invoke the same structural parameters Table 1 Parameters of SoC control strategy from models of ADVISOR operated in the initial Power demand/require Pd/req simulation. The significant part of SoC strategy control will be built in Simulink Stateflow to Power from engine Peng optimize the powertrain control for the whole hybrid High and low point SoC SoC , SoC h l system. Power from engine to charge P battery eng2b 3.1 The model setting Instantaneous SoC (power stored SoCi (Pb,i) The initial simulation model setting adopts the in battery) models of the first generation Toyota Prius in Maximum and minimum rated P , P ADVISOR. The significant parameters for the power of battery b,max b,min ISSN: 2534-8876 22 Volume 2, 2017 International Journal of Transportation Systems Ning Ding, K. Prasad, T. T. Lie http://www.iaras.org/iaras/journals/ijts operation window set in initial ADVISOR model are SoC control strategy set in Stateflow invoked same listed in Table 2. The crucial model of SoC strategy parameters with initial model. The simulation results control logic state designed in the second part has orderly showed in Fig.6 and Table 3 compares the been built in Simulink Stateflow logic chart, in which specific output values from different emission the critical value of SoCh and SoCl are set to 0.80 and characteristics. The emission of CO considerably 0.30, respectively. reduced by 26.4% and the NOx reduced by 3.6%. There is, however, an increase in HC by 5.3%. Table 2 Parameters of ADVISOR model setting in simulation Motor: 1.5 L Straight-4|I4 DOHC 16 valve; 43 kW (58 Engine hp) at 4000 rpm Torque: 102 N·m (75 lbf·ft) at 4000 rpm Motor: 288 V 30 kW; 31 kW (40 hp) at 940-2000 rpm Electric Torque: 305 N·m (225 lbf·ft) at 0-940 rpm NiMH battery with maximum Energy Storage 40 kW power Planetary gear continuously Transmission variable transmission model the constant coefficient of Wheel/Axle rolling resistance model (a) Initial model in ADVISOR constant power accessory Accessory load models Override mass 1368 kg 3.2 Simulation and discussion The parameters of initial conditions included several characteristics that can be adjusted from model blocks. For example, the value of the coefficient of air resistance is 0.3, wheel radius is 0.287m, wind 2 award area is 1.746m and the initial SoC0 is 0.75. The input drive cycle adopted the Extra Urban Driving Cycle from a database of Economic Commission for Europe (CYC_ECE_EUDC) shown in Fig.5. (b) SoC strategy design build by Stateflow in MATLAB/Simulink Fig.6 The simulation results Moreover, the simulation results showed that fuel economy (mpg) increased from 45.4 to 57.9 under the SoC control strategy. In terms of logic design, the engine works as the main power provider only in CSeng2b mode. In CD or CSb mode, the engine Fig.5 The speed input of CYC_ECE_EUDC works as a parallel or auxiliary part to provide little propulsion. In other words, the vehicle performance With the input of CYC_ECE_EUDC, the simulation has been improved considerably through the SoC was conducted the initial Prius model in ADVISOR control strategy. running in MATLB/Simulink environment, while the ISSN: 2534-8876 23 Volume 2, 2017 International Journal of Transportation Systems Ning Ding, K.
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